Claude Sonnet 4 vs SAM 3

Compare Claude Sonnet 4 and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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AnthropicClaude Sonnet 4
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MetaSAM 3
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Models in this comparison

Meta

Claude Sonnet 4 vs SAM 3: Overview

Claude Sonnet 4

Claude 4 Sonnet, released by Anthropic in May 2025, is the mid-tier model in the Claude 4 family, designed to balance capability, cost, and speed. It is multimodal, accepting both text and images, and extends beyond prior versions with improved “computer use” support, allowing API-driven interaction with desktop-like interfaces. By default, it supports 200,000 tokens of context, but as of August 2025, it also offers a 1 million-token context window in public beta—making it one of the most context-capable models available for processing entire codebases or large document sets in a single request.

Sonnet 4 is significantly cheaper than the flagship Opus while still demonstrating strong reasoning, coding, and instruction-following ability with reduced hallucinations. Its extended context capabilities and lower latency make it well-suited for enterprise-scale knowledge management, software development, research assistants, and productivity automation where both cost efficiency and high reliability are essential.

SAM 3

Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.

Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.

Claude Sonnet 4 vs SAM 3 Comparison Table

PropertyClaude Sonnet 4SAM 3
OrganizationAnthropicMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2025Nov 2025
Context Window1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00
Output $/1M$15.00
Vision Tasks
Object DetectionDemoDemo
CaptioningDemo
ClassificationDemo
Instance Segmentation
OCRDemo
Promptable Concept SegmentationDemo
Video Object Tracking
Vision Language
Visual Question AnsweringDemo
Zero Shot Segmentation
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Zero-shot Detection
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
68.66%
Avg Response Time21.26s
Defect Detection
80%(12/15)
Document Understanding
88.9%(8/9)
Object Counting
20%(2/10)
Object Understanding
78.6%(11/14)
Spatial Understanding
68.4%(13/19)